code: def test_symbols_std(sparse_data):
      X, y, categorical = sparse_data
      mf = meta_features.metafeatures["SymbolsSTD"](
          X, y, logging.getLogger("Meta"), categorical
      )
      symbol_frequency = [2, 6, 1, 3, 3, 1, 1, 2, 1, 1, 2, 2]
      assert pytest.approx(mf.value) == np.std(symbol_frequency)
  
imports: import logging
  import os
  import arff
  import numpy
  from scipy import sparse
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  import autosklearn.metalearning.metafeatures.metafeatures
  from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import pytest
label_desc: Use image node_id 13 for calling a global function with example usage: test_symbols_std(sparse_data) without return types